Currently, communication can occur through many mediums, including email, online news services, feeds, instant messaging, texting, and social networking sites. Social networking sites, such as Facebook, Twitter, or Google Reader, provide communication through streams of messages, which are either composed by, or transmitted to, a user. Many of the social networking sites limit the number of characters in each text entry, which can result in multiple messages regarding a single topic. Individuals interact with the user by subscribing to the user's stream or by transmitting a text entry to the user.
As the popularity of social networking sites increases, the number of messages transmitted daily also increase. For example, the number of tweets transmitted per hour via Twitter ranges from 400,000 to 1,400,000. Due to the number of messages transmitted, users are having difficulty reviewing all the messages received. Sorting through and reviewing received messages can be very time consuming, especially after a long period of time away. Many messages received are related to social aspects, rather than substantive interesting information. The messages can include links to photographs, articles, or other Websites, which can include substantively relevant and interesting information. However, failure to review all messages can result in missing important or interesting information.
Attempts to generate recommendations from explicit social information have been made, such as by Hill et al. in “Using Frequency-Of-Mention In Public Conversations For Social Filtering.” In Proc. of CSCW 1996. A social filtering system that recommends news URLs on Usenet newsgroups is provided. The system works as a within-group popular voice. For example, in each group of content, the most popular URLs are recommended based on a “one person, one vote” basis. The more people in a group who mention a URL, the more likely the URL will be recommended. However, Hill fails to consider relationships between members in the newsgroups.
Therefore, there is a need for proactively providing content recommendations to users, which are selected from an information stream.